Advanced Traffic Flow Optimization Using Hybrid Machine Learning and Deep Learning Techniques

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The hybrid ensemble model outperformed traditional baselines, achieving a peak R² of 0.954 and an RMSE of 0.045.
Statistical significance testing confirmed reliable improvements, with temporal features dominating during high-variance periods.
Anomaly detection and temporal feature engineering were critical in the preprocessing pipeline for this traffic flow model.
Computational overhead and data sparsity during rare events are notable limitations, impacting the model's applicability.

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This study demonstrates predictive accuracy improvements in traffic flow optimization using machine learning and deep learning techniques.

Ismanto et al. (2025) studied this question.

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This study demonstrates predictive accuracy improvements in traffic flow optimization using machine learning and deep learning techniques.